{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RUL prediction using random forest\n",
    "\n",
    "In this notebook, we will apply random forest to predict RUL of NASA's turbofan engine dataset FD003. We will use scikit-learn to implement random forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(324)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy version:  1.18.5\n",
      "Pandas version:  1.0.5\n",
      "Scikit-learn version:  0.23.1\n"
     ]
    }
   ],
   "source": [
    "print(\"Numpy version: \", np.__version__)\n",
    "print(\"Pandas version: \", pd.__version__)\n",
    "print(\"Scikit-learn version: \", sklearn.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "\n",
    "We strongly encourage readers to go through the [dataset description and prreprocessing notebook](https://github.com/biswajitsahoo1111/rul_codes_open/blob/master/notebooks/cmapss_notebooks/CMAPSS_data_description_and_preprocessing.ipynb). In that notebook we have explained how data preprocessing functions work with simple examples. In this notebook we will only use those functions. So prior familiarity with these functions is an advantage. Below are the parameters that we will use for data preprocessing:\n",
    "\n",
    "* Degradation model: Piecewise linear\n",
    "* Early RUL: 125\n",
    "* Window length: 1\n",
    "* Shift: 1\n",
    "* Data scaling: No (Tree based methods don't require data scaling)\n",
    "\n",
    "We will calculate two prediction scores on test data. In one case, we will take last 5 examples of test data for engine, calculate their predictions, and finally average those for each engine. In the second case, we will take only the last example of each engine and make predictions. The logic behind taking last 5 examples and averaging their predictions is to make the prediction robust against outliers. Due to some external factor, if our last example happens to be corrupted, its prediction outcome might be far off from the actual one. But if we average predictions from last 5 examples, we will get a more conservative estimate. \n",
    "\n",
    "In the following cell we will show boxplots of each column of training data. That will give us an idea about the values in different columns. If all the values in a column are constant, we drop those columns from our analysis.\n",
    "\n",
    "Readers can download the data from [here](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#turbofan). In the following cells, wherever data are read from a folder, readers should change the string to point to the respective folder from their system to run this notebook seamlessly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1512 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"/home/biswajit/data/cmapss_data/train_FD003.txt\", sep= \"\\s+\", header = None)\n",
    "plt.figure(figsize = (16, 21))\n",
    "for i in range(21):\n",
    "    temp_data = train_data.iloc[:,i+5]\n",
    "    plt.subplot(7,3,i+1)\n",
    "    plt.boxplot(temp_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_targets(data_length, early_rul = None):\n",
    "    \"\"\" \n",
    "    Takes datalength and earlyrul as input and \n",
    "    creates target rul.\n",
    "    \"\"\"\n",
    "    if early_rul == None:\n",
    "        return np.arange(data_length-1, -1, -1)\n",
    "    else:\n",
    "        early_rul_duration = data_length - early_rul\n",
    "        if early_rul_duration <= 0:\n",
    "            return np.arange(data_length-1, -1, -1)\n",
    "        else:\n",
    "            return np.append(early_rul*np.ones(shape = (early_rul_duration,)), np.arange(early_rul-1, -1, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_input_data_with_targets(input_data, target_data = None, window_length = 1, shift = 1):\n",
    "    \"\"\"Depending on values of window_length and shift, this function generates batchs of data and targets \n",
    "    from input_data and target_data.\n",
    "    \n",
    "    Number of batches = np.floor((len(input_data) - window_length)/shift) + 1\n",
    "    \n",
    "    **We don't check input dimensions uisng exception handling. So readers should be careful while using these\n",
    "    functions. If input data are not of desired dimension, either error occurs or something undesirable is \n",
    "    produced as output.**\n",
    "    \n",
    "    Arguments:\n",
    "        input_data: input data to function (Must be 2 dimensional)\n",
    "        target_data: input rul values (Must be 1D array)s\n",
    "        window_length: window length of data\n",
    "        shift: Distance by which the window moves for next batch. This is closely related to overlap\n",
    "               between data. For example, if window length is 30 and shift is 1, there is an overlap of \n",
    "               29 data points between two consecutive batches.\n",
    "        \n",
    "    \"\"\"\n",
    "    num_batches = np.int(np.floor((len(input_data) - window_length)/shift)) + 1\n",
    "    num_features = input_data.shape[1]\n",
    "    output_data = np.repeat(np.nan, repeats = num_batches * window_length * num_features).reshape(num_batches, window_length,\n",
    "                                                                                                  num_features)\n",
    "    if target_data is None:\n",
    "        for batch in range(num_batches):\n",
    "            output_data[batch,:,:] = input_data[(0+shift*batch):(0+shift*batch+window_length),:]\n",
    "        return output_data\n",
    "    else:\n",
    "        output_targets = np.repeat(np.nan, repeats = num_batches)\n",
    "        for batch in range(num_batches):\n",
    "            output_data[batch,:,:] = input_data[(0+shift*batch):(0+shift*batch+window_length),:]\n",
    "            output_targets[batch] = target_data[(shift*batch + (window_length-1))]\n",
    "        return output_data, output_targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_test_data(test_data_for_an_engine, window_length, shift, num_test_windows = 1):\n",
    "    \"\"\" This function takes test data for an engine as first input. The next two inputs\n",
    "    window_length and shift are same as other functins. \n",
    "    \n",
    "    Finally it takes num_test_windows as the last input. num_test_windows sets how many examplles we\n",
    "    want from test data (from last). By default it extracts only the last example.\n",
    "    \n",
    "    The function return last examples and number of last examples (a scaler) as output. \n",
    "    We need the second output later. If we are extracting more than 1 last examples, we have to \n",
    "    average their prediction results. The second scaler halps us do just that.\n",
    "    \"\"\"\n",
    "    max_num_test_batches = np.int(np.floor((len(test_data_for_an_engine) - window_length)/shift)) + 1\n",
    "    if max_num_test_batches < num_test_windows:\n",
    "        required_len = (max_num_test_batches -1)* shift + window_length\n",
    "        batched_test_data_for_an_engine = process_input_data_with_targets(test_data_for_an_engine[-required_len:, :],\n",
    "                                                                          target_data = None,\n",
    "                                                                          window_length = window_length, shift = shift)\n",
    "        return batched_test_data_for_an_engine, max_num_test_batches\n",
    "    else:\n",
    "        required_len = (num_test_windows - 1) * shift + window_length\n",
    "        batched_test_data_for_an_engine = process_input_data_with_targets(test_data_for_an_engine[-required_len:, :],\n",
    "                                                                          target_data = None,\n",
    "                                                                          window_length = window_length, shift = shift)\n",
    "        return batched_test_data_for_an_engine, num_test_windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed trianing data shape:  (24720, 1, 15)\n",
      "Processed training ruls shape:  (24720,)\n",
      "Processed test data shape:  (500, 1, 15)\n",
      "True RUL shape:  (100,)\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.read_csv(\"/home/biswajit/data/cmapss_data/test_FD003.txt\", sep = \"\\s+\", header = None)\n",
    "true_rul = pd.read_csv('/home/biswajit/data/cmapss_data/RUL_FD003.txt', sep = '\\s+', header = None)\n",
    "\n",
    "window_length = 1\n",
    "shift = 1\n",
    "early_rul = 125            \n",
    "processed_train_data = []\n",
    "processed_train_targets = []\n",
    "\n",
    "# How many test windows to take for each engine. If set to 1 (this is the default), only last window of test data for \n",
    "# each engine are taken. If set to a different number, that many windows from last are taken. \n",
    "# Final output is the average of output of all windows.\n",
    "num_test_windows = 5     \n",
    "processed_test_data = []\n",
    "num_test_windows_list = []\n",
    "\n",
    "columns_to_be_dropped = [0,1,2,3,4,5,9,14,20,22,23]\n",
    "\n",
    "num_machines = np.min([len(train_data[0].unique()), len(test_data[0].unique())])\n",
    "\n",
    "for i in np.arange(1, num_machines + 1):\n",
    "    \n",
    "    temp_train_data = train_data[train_data[0] == i].drop(columns=columns_to_be_dropped).values\n",
    "    temp_test_data = test_data[test_data[0] == i].drop(columns=columns_to_be_dropped).values\n",
    "    \n",
    "    # Verify if data of given window length can be extracted from both training and test data\n",
    "    if (len(temp_test_data) < window_length):\n",
    "        print(\"Test engine {} doesn't have enough data for window_length of {}\".format(i, window_length))\n",
    "        raise AssertionError(\"Window length is larger than number of data points for some engines. \"\n",
    "                             \"Try decreasing window length.\")\n",
    "    elif (len(temp_train_data) < window_length):\n",
    "        print(\"Train engine {} doesn't have enough data for window_length of {}\".format(i, window_length))\n",
    "        raise AssertionError(\"Window length is larger than number of data points for some engines. \"\n",
    "                             \"Try decreasing window length.\")\n",
    "    \n",
    "    temp_train_targets = process_targets(data_length = temp_train_data.shape[0], early_rul = early_rul)\n",
    "    data_for_a_machine, targets_for_a_machine = process_input_data_with_targets(temp_train_data, temp_train_targets, \n",
    "                                                                                window_length = window_length, shift = shift)\n",
    "    \n",
    "    # Prepare test data\n",
    "    test_data_for_an_engine, num_windows = process_test_data(temp_test_data, window_length = window_length, shift = shift,\n",
    "                                                             num_test_windows = num_test_windows)\n",
    "    \n",
    "    processed_train_data.append(data_for_a_machine)\n",
    "    processed_train_targets.append(targets_for_a_machine)\n",
    "    \n",
    "    processed_test_data.append(test_data_for_an_engine)\n",
    "    num_test_windows_list.append(num_windows)\n",
    "\n",
    "processed_train_data = np.concatenate(processed_train_data)\n",
    "processed_train_targets = np.concatenate(processed_train_targets)\n",
    "processed_test_data = np.concatenate(processed_test_data)\n",
    "true_rul = true_rul[0].values\n",
    "\n",
    "# Shuffle data\n",
    "index = np.random.permutation(len(processed_train_targets))\n",
    "processed_train_data, processed_train_targets = processed_train_data[index], processed_train_targets[index]\n",
    "\n",
    "print(\"Processed trianing data shape: \", processed_train_data.shape)\n",
    "print(\"Processed training ruls shape: \", processed_train_targets.shape)\n",
    "print(\"Processed test data shape: \", processed_test_data.shape)\n",
    "print(\"True RUL shape: \", true_rul.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed train data shape:  (24720, 15)\n",
      "Processed test data shape:  (500, 15)\n"
     ]
    }
   ],
   "source": [
    "processed_train_data = processed_train_data.reshape(-1, processed_train_data.shape[2])\n",
    "processed_test_data = processed_test_data.reshape(-1, processed_test_data.shape[2])\n",
    "print(\"Processed train data shape: \", processed_train_data.shape)\n",
    "print(\"Processed test data shape: \", processed_test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  20.650325661785043\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor(n_estimators= 150, max_features = \"sqrt\",\n",
    "                                 n_jobs = -1, random_state = 8)\n",
    "rf_model.fit(processed_train_data, processed_train_targets)\n",
    "rul_pred = rf_model.predict(processed_test_data)\n",
    "\n",
    "# First split predictions according to number of windows of each engine\n",
    "preds_for_each_engine = np.split(rul_pred, np.cumsum(num_test_windows_list)[:-1])\n",
    "mean_pred_for_each_engine = [np.average(ruls_for_each_engine, weights = np.repeat(1/num_windows, num_windows)) \n",
    "                             for ruls_for_each_engine, num_windows in zip(preds_for_each_engine, num_test_windows_list)]\n",
    "RMSE = np.sqrt(mean_squared_error(true_rul, mean_pred_for_each_engine))\n",
    "print(\"RMSE: \", RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With somewhat arbitrary choice of hyperparameters, we obtained an RMSE value of 20.65. But can we do better? To find out better values of hyperparameters, we can do a principled hyperparameter tuning. There are different ways to do hyperparameter search. Here, we will use simple grid search. \n",
    "\n",
    "In grid search, we first define a grid of parameters. Then for each set of parameters, we will fit a 10 xgboost models (as it is 10 fold cross validation). Finally we will average the result of all folds for a particular parameter choice. The parameter choice that gives best score for cross validation is chosen as the best hyperparameter.\n",
    "\n",
    "Grid search of hyperparameters (with cross validation) is computationally intensive. It might take a long time on a personal computer. If that is the case, readers are advised to comment the next cell and directly use the best hyperparameter values as done in subsequent cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "             param_grid={'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'n_estimators': [100, 150, 200, 250, 300, 350]},\n",
       "             scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {\"n_estimators\": [100, 150, 200, 250, 300, 350],\n",
    "              \"max_features\": [\"auto\", \"sqrt\", \"log2\"]}\n",
    "grid = GridSearchCV(RandomForestRegressor(), param_grid = param_grid,scoring = \"neg_root_mean_squared_error\",\n",
    "                    n_jobs = -1, cv = 10)\n",
    "grid.fit(processed_train_data, processed_train_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 'log2', 'n_estimators': 350}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the best model to predict on test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE after hyperparameter tuning:  20.53826611357046\n"
     ]
    }
   ],
   "source": [
    "best_rf_model = grid.best_estimator_\n",
    "rul_pred_tuned = best_rf_model.predict(processed_test_data)\n",
    "\n",
    "preds_for_each_engine_tuned = np.split(rul_pred_tuned, np.cumsum(num_test_windows_list)[:-1])\n",
    "mean_pred_for_each_engine_tuned = [np.average(ruls_for_each_engine, weights = np.repeat(1/num_windows, num_windows)) \n",
    "                                   for ruls_for_each_engine, num_windows in zip(preds_for_each_engine_tuned,\n",
    "                                                                                num_test_windows_list)]\n",
    "RMSE_tuned = np.sqrt(mean_squared_error(true_rul, mean_pred_for_each_engine_tuned))\n",
    "print(\"RMSE after hyperparameter tuning: \", RMSE_tuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that while prediction, we are predicting RUL values for last 5 examples of every engine. Then we take mean of all 5 predictions for each engine and calculate final RMSE.\n",
    "\n",
    "If instead we wish to take only the last example of every engine to make predictions and calculate RUL, we can do so by taking the last prediction of every engine as calculated before and calculate RMSE as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Taking only last examples):  20.58630021571211\n"
     ]
    }
   ],
   "source": [
    "indices_of_last_examples = np.cumsum(num_test_windows_list) - 1\n",
    "preds_for_last_example = np.concatenate(preds_for_each_engine_tuned)[indices_of_last_examples]\n",
    "\n",
    "RMSE_new = np.sqrt(mean_squared_error(true_rul, preds_for_last_example))\n",
    "print(\"RMSE (Taking only last examples): \", RMSE_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are not convinced by above calculations, take a look at the last section of [this notebook](https://github.com/biswajitsahoo1111/rul_codes_open/blob/master/notebooks/cmapss_notebooks/CMAPSS_FD001_xgboost_piecewise_linear_degradation_model.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For CMAPSS data, along with RMSE another metric (S-score) is usually reported in literature. S-score is defined as:\n",
    "\n",
    "$$S= \\sum_{i=1}^N{s_i}$$\n",
    "\n",
    "where, \n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "    s_i=\n",
    "    \\begin{cases}\n",
    "      (e^{-\\frac{d_i}{13}})-1, & \\text{for}\\ d_i < 1 \\\\\n",
    "      (e^{\\frac{d_i}{10}})-1, & \\text{for}\\ d_i \\geq 1\\\\\n",
    "    \\end{cases}\n",
    "  \\end{equation}\n",
    "  $$\n",
    "  \n",
    "We can compute the S-metric as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_s_score(rul_true, rul_pred):\n",
    "    \"\"\"\n",
    "    Both rul_true and rul_pred should be 1D numpy arrays.\n",
    "    \"\"\"\n",
    "    diff = rul_pred - rul_true\n",
    "    return np.sum(np.where(diff < 0, np.exp(-diff/13)-1, np.exp(diff/10)-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S-score:  1774.4377653569911\n"
     ]
    }
   ],
   "source": [
    "s_score = compute_s_score(true_rul, preds_for_last_example)\n",
    "print(\"S-score: \", s_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot true and predicted RUL values\n",
    "plt.plot(true_rul, label = \"True RUL\", color = \"red\")\n",
    "plt.plot(preds_for_last_example, label = \"Pred RUL\", color = \"blue\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot variable importance score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = best_rf_model.feature_importances_\n",
    "indices = np.argsort(feature_importance)[::-1]\n",
    "num_features = processed_train_data.shape[1]\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(num_features), feature_importance[indices],\n",
    "        color=\"blue\", align=\"center\")\n",
    "plt.xticks(range(num_features), indices)\n",
    "plt.xlim([-1, num_features])\n",
    "plt.xlabel(\"Feature Columns\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a final note remember that hyperparameter tuning is more of an art than science. It is possible to obtain better results than what has been obtained here by choosing better set of hyperparameters.\n",
    "\n",
    "For other reproducible results on RUL, interested readers can visit my [project page](https://biswajitsahoo1111.github.io/rul_codes_open). "
   ]
  }
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